Providing a personalized navigation experience in a mobile application

• US 9,465,522 B2
• Filed: 03/29/2013
• Issued: 10/11/2016
• Est. Priority Date: 03/28/2013
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: LinkedIn Corporation (Microsoft Corporation)
• Inventors: Prasad, Srikiran, Gupta, Akhilesh, Cohen, Tomer, McGuire, Leah
• Primary Examiner(s): SALOMON, PHENUEL S
• Application Number:
  US13/853,948
• Publication Number:
  US 20140298194A1
• Time in Patent Office: 1,292 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06Q 30/0241 Advertisements

Abstract

The disclosed embodiments relate to a system for presenting navigation options to a user of a mobile application. During operation, the system receives usage data comprising a record of actions performed while the user was interacting with the mobile application. Next, the system analyzes the usage data to identify areas of interest within the mobile application that the user is likely to access. The system then constructs a set of personalized navigation options for the user based on the identified areas of interest, and possibly other areas of the application based on promotional considerations. Finally, the system outputs the set of personalized navigation options to be presented to the user through a navigation pane in the mobile application, wherein the navigation pane includes shortcuts to the set of personalized navigation options.

Claims

1. A computer-implemented method for presenting navigation options to a user of a mobile application, the method comprising:

receiving usage data of the user comprising mobile usage metrics and desktop usage metrics, wherein the mobile and desktop usage metrics comprise a record of actions performed while the user was interacting with the mobile application and a corresponding desktop application, and wherein the mobile usage metrics are associated with a mobile scale factor that is higher than a desktop scale factor associated with the desktop usage metrics;

analyzing the usage data to identify areas of interest within the mobile application that the user is likely to access, wherein an area of interest is associated with a product group, wherein a product group is associated with portions of the mobile application that provide a specific type of functionality or content, and wherein the mobile usage metrics are weighted more than the desktop usage metrics when identifying the areas of interest;

constructing a set of personalized navigation options for the user based on the identified areas of interest; and

outputting the set of personalized navigation options to be presented to the user through a navigation pane in the mobile application, wherein the navigation pane includes shortcuts to the set of personalized navigation options.

2. The computer-implemented method of claim 1, wherein the method is performed by a server associated with the mobile application, and wherein outputting the set of personalized navigation options involves sending the set of personalized navigation options to the mobile application.

3. The computer-implemented method of claim 1, wherein the method is performed by the mobile device.

4. The computer-implemented method of claim 1, wherein constructing the set of personalized navigation options also involves including navigation options associated with other areas of the application based on promotional considerations instead of usage data.

5. The computer-implemented method of claim 1, wherein the navigation pane is one of:

an ordered list containing shortcuts to the set of personalized navigation options; and

an ordered grid containing shortcuts to the set of personalized navigation options.

6. The computer-implemented method of claim 1, wherein the navigation pane includes an add-shortcut option, which when selected presents a comprehensive list of possible navigation options to the user.

7. The computer-implemented method of claim 1, wherein if the navigation pane includes a new shortcut that did not appear in a previous navigation pane, the new shortcut is highlighted to make the user aware that the shortcut is new.

8. The computer-implemented method of claim 1,

wherein the mobile application acts as an interface to an online application that can also be accessed through a browser-based web interface;

and

wherein the usage data also includes usage data obtained through the web interface.

9. The computer-implemented method of claim 1, wherein the usage data also includes usage data for related users, wherein the related users can include users that are connected with the user through links in an online social-networking application, or users who have a similar personal profile to the user.

10. The computer-implemented method of claim 1, wherein the method is performed periodically so that the set of personalized navigation options dynamically evolves over time.

11. The computer-implemented method of claim 1, wherein analyzing the usage data involves using a machine-learning technique to analyze the usage data to identify the areas of interest for the user.

12. The computer-implemented method of claim 1, wherein the usage data includes a record of page loads and user actions that occurred while the user was interacting with the mobile application.

13. The computer-implemented method of claim 1, wherein the shortcuts in the navigation pane are presented in an ordering that is based on likelihood of user access.

14. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for presenting navigation options to a user of a mobile application, the method comprising:

receiving usage data of the user comprising mobile usage metrics and desktop usage metrics, wherein the mobile and desktop usage metrics comprise a record of actions performed while the user was interacting with the mobile application and a corresponding desktop application, and wherein the mobile usage metrics are associated with a mobile scale factor that is higher than a desktop scale factor associated with the desktop usage metrics;

analyzing the usage data to identify areas of interest within the mobile application that the user is likely to access, wherein an area of interest is associated with a product group, wherein a product group is associated with portions of the mobile application that provide a specific type of functionality or content, and wherein the mobile usage metrics are weighted more than the desktop usage metrics when identifying the areas of interest;

constructing a set of personalized navigation options for the user based on the identified areas of interest; and

outputting the set of personalized navigation options to be presented to the user through a navigation pane in the mobile application, wherein the navigation pane includes shortcuts to the set of personalized navigation options.

15. The non-transitory computer-readable storage medium of claim 14, wherein constructing the set of personalized navigation options also involves including navigation options associated with other areas of the application based on promotional considerations instead of usage data.

16. The non-transitory computer-readable storage medium of claim 14, wherein the navigation pane includes an add-shortcut option, which when selected presents a comprehensive list of possible navigation options to the user.

17. The non-transitory computer-readable storage medium of claim 14,

wherein the mobile application acts as an interface to an online application that can also be accessed through a browser-based web interface;

and

wherein the usage data also includes usage data obtained through the web interface.

18. The non-transitory computer-readable storage medium of claim 14, wherein the usage data also includes usage data for related users, wherein the related users can include users that are connected with the user through links in an online social-networking application, or users who have a similar personal profile to the user.

19. The non-transitory computer-readable storage medium of claim 14, wherein the method is performed periodically so that the set of personalized navigation options dynamically evolves over time.

20. The non-transitory computer-readable storage medium of claim 14, wherein analyzing the usage data involves using a machine-learning technique to analyze the usage data to identify the areas of interest for the user.

21. A system that facilitates presenting navigation options to a user of a mobile application, comprising:

a processor;

a memory; and

code that executes using the processor and the memory, wherein the code is configured to,

receive usage data of the user comprising mobile usage metrics and desktop usage metrics, wherein the mobile and desktop usage metrics comprises a record of actions performed while the user was interacting with the mobile application and a corresponding desktop application, and wherein the mobile usage metrics are associated with a mobile scale factor that is higher than a desktop scale factor associated with the desktop usage metrics;

analyze the usage data to identify areas of interest within the mobile application that the user is likely to access, wherein an area of interest is associated with a product group, wherein a product group is associated with portions of the mobile application that provide a specific type of functionality or content, and wherein the mobile usage metrics are weighted more than the desktop usage metrics when identifying the areas of interest;

construct a set of personalized navigation options for the user based on the identified areas of interest; and

output the set of personalized navigation options to be presented to the user through a navigation pane in the mobile application, wherein the navigation pane includes shortcuts to the set of personalized navigation options.